方法对比
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| 在线迁移学习× | 在线学习× | |
|---|---|---|
| 领域 | 机器学习 | 机器学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2010 | 1958–2000s |
| 提出者≠ | Zhao, P. & Hoi, S. C. H. | Rosenblatt, F.; Littlestone, N.; Shalev-Shwartz, S. (key contributors) |
| 类型≠ | Online learning with source-domain knowledge transfer | Learning paradigm (sequential model update) |
| 开创性文献≠ | Zhao, P., & Hoi, S. C. H. (2010). OTL: A Framework of Online Transfer Learning. In Proceedings of the 27th International Conference on Machine Learning (ICML 2010), pp. 1231–1238. Omnipress. link ↗ | Shalev-Shwartz, S. (2011). Online Learning and Online Convex Optimization. Foundations and Trends in Machine Learning, 4(2), 107–194. DOI ↗ |
| 别名 | OTL, streaming transfer learning, incremental transfer learning, online domain adaptation | incremental learning, sequential learning, streaming learning, online machine learning |
| 相关≠ | 4 | 6 |
| 摘要≠ | Online Transfer Learning (OTL) extends transfer learning to sequential, streaming settings: instead of training on a fixed dataset, the model processes examples one at a time and simultaneously leverages knowledge from a related source domain to improve predictions on the target domain without requiring large labeled target datasets upfront. | Online learning is a machine learning paradigm in which a model is updated incrementally as each new data point arrives, rather than being trained once on a fixed dataset. It is essential when data streams continuously, storage is limited, or the underlying distribution shifts over time. Theoretical performance is measured by cumulative regret relative to the best fixed predictor in hindsight. |
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